The present invention relates to training of cognitive computing systems, and more specifically, to techniques and mechanisms for improving the results generated by a Question and Answer system by generating enriched ground truth and providing the enriched ground truth for training the system.
With the increased usage of computing networks, such as the Internet, users can easily be overwhelmed with the amount of information available from various structured and unstructured sources. However, information gaps abound as users try to piece together what they believe to be relevant during searches for information on various subjects. To assist with such searches, research has been directed to creating cognitive systems such as Question and Answer (QA) systems that take an input question, analyze the question, and return results indicative of the most probable answer or answers to the input question. QA systems provide automated mechanisms for searching through large sets of sources of content, e.g., electronic documents, and analyze them with regard to an input question to determine an answer to the question and a confidence measure as to how accurate an answer to the question might be.
The quality of the responses provided by a QA system is tied to the training provided to the system. When a cognitive system is trained, ground truth is provided to the system. The quality of system training, and in turn, the quality of the cognitive system is determined by the quality of the ground truth used to train the system. Therefore, the more comprehensive the ground truth, the higher the quality of the system training.